Genetic Algorithm and Particle Swarm Optimization Techniques in Supply Chain Design Problems: A Survey

Genetic Algorithm and Particle Swarm Optimization Techniques in Supply Chain Design Problems: A Survey

Md. Ashikur Rahman (Universiti Teknologi PETRONAS, Malaysia), Pandian M. Vasant (Universiti Teknologi PETRONAS, Malaysia), Junzo Watada (Universiti Teknologi PETRONAS, Malaysia) and Rajalingam Al Sokkalingam (Universiti Teknologi PETRONAS, Malaysia)
DOI: 10.4018/978-1-7998-3645-2.ch019
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Metaheuristics has become a top research area. Numerous optimization problems have been solved by metaheuristics as they showed comprehensive improvements to solve these intractable optimization problems. Complex problems like supply chain design problems need strategic decisions, and metaheuristics can intensify the decisions while designing supply chain network. In this chapter, the authors have introduced how nature memetic algorithms (e.g., genetic algorithm and particle swarm algorithms) are implemented to solve supply chain network design problem. A discussion about the recent research in this field shows an important direction to the future research.
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Background Study About Ga And Pso

Altiparmak et al.(2006) discussed a distribution network chain combined with three stage. Initially they introduced a mathematical model, consequently their solution-based approach was defined by Genetic Algorithm. Sue et al. (2008) solved a complex product distribution problem through GA, Tabu search and improved Path relinking method. Thanh et al. (2008) discussed a multi- product and multi planning based distribution network. They first formulated a cost reduction model through MILPM. Calvete and Gale (2010) proposed a multi-objective model for supply chain planning. Their model was formulated with multi producer, multi distribution centres along with multi retailers. Wang et al. (2011) elaborated a single echelon supply chain network through a Mixed Integer Linear Programming (MILP) model. The considered factor of the model was stochastic demand. Particularly that profit based model was solved by Genetic algorithm along with efficient greedy heuristic. Amorim et al. (2012) introduced a hybrid GA model to solve a distribution network problem for the perishable products. Kadadevaramath et al. (2012) designed a 2-echelon distribution network. Their solution approach was a comparative study between GA and PSO. Varthanan et al. (2012) presented a stochastic demand-based distribution network. A MILP model was applied to reduce the cost of that supply chain network. Zamarripa et al. (2012) designed a Multi period supply chain network. They formulated a cost reduction model by MILP and found the solution by GA. Raa et al. (2013) introduced a MILP model for an established Product Distribution Network (PDN). The model was optimized by a metaheuristic algorithm which has a wide use of expounding large instance problems. Nasiri et al. (2014) discussed a multistage distribution planning considering demand uncertainty. Alike majority of the distribution network model they proposed a cost reduction model. Later, a heuristic method based on Genetic Algorithm.

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